{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "11d8810e-c1b2-458c-bf28-ecab44a01cc2",
   "metadata": {
    "tags": []
   },
   "source": [
    "\n",
    "# Set Cover Problem\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "877ae3cf-10cc-4e51-a428-eb57687d8d18",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "The set cover problem [[1]](#SetCoverWiki) represents a well-known problem in the fields of combinatorics, computer science, and complexity theory. It is an NP-complete problems.\n",
    "\n",
    "The problem presents us with a universal set, $\\displaystyle U$, and a collection $\\displaystyle S$ of subsets of $\\displaystyle U$. The goal is to find the smallest possible subfamily, $\\displaystyle C \\subseteq S$, whose union equals the universal set.\n",
    "\n",
    "Formally, let's consider a universal set $\\displaystyle U = {1, 2, ..., n}$ and a collection $\\displaystyle S$ containing $m$ subsets of $\\displaystyle U$, $\\displaystyle S = {S_1, ..., S_m}$ with $\\displaystyle S_i \\subseteq U$. The challenge of the set cover problem is to find a subset $\\displaystyle C$ of $\\displaystyle S$ of minimal size such that $\\displaystyle \\bigcup_{S_i \\in C} S_i = U$.\n",
    "\n",
    "# Solving with the Classiq platform\n",
    "\n",
    "We go through the steps of solving the problem with the Classiq platform, using QAOA algorithm [[2](#QAOA)]. The solution is based on defining a pyomo model for the optimization problem we would like to solve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "49a9588b-e79e-4813-b7c5-ac068d7b930c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:36:44.114329Z",
     "iopub.status.busy": "2024-05-07T15:36:44.114069Z",
     "iopub.status.idle": "2024-05-07T15:36:45.009877Z",
     "shell.execute_reply": "2024-05-07T15:36:45.009179Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from typing import cast\n",
    "\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pyomo.core as pyo\n",
    "from IPython.display import Markdown, display\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8517ef73-faf6-4fd8-9db8-4088551398e0",
   "metadata": {},
   "source": [
    "## Building the Pyomo model from a graph input\n",
    "\n",
    "We proceed by defining the pyomo model that will be used on the Classiq platform, using the mathematical formulation defined above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "48889b21-557b-481c-80c5-3c0b5c91adb6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:36:45.021711Z",
     "iopub.status.busy": "2024-05-07T15:36:45.014097Z",
     "iopub.status.idle": "2024-05-07T15:36:45.033939Z",
     "shell.execute_reply": "2024-05-07T15:36:45.033189Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "from typing import List\n",
    "\n",
    "import pyomo.core as pyo\n",
    "\n",
    "\n",
    "def set_cover(sub_sets: List[List[int]]) -> pyo.ConcreteModel:\n",
    "    entire_set = set(itertools.chain(*sub_sets))\n",
    "    n = max(entire_set)\n",
    "    num_sets = len(sub_sets)\n",
    "    assert entire_set == set(\n",
    "        range(1, n + 1)\n",
    "    ), f\"the union of the subsets is {entire_set} not equal to range(1, {n + 1})\"\n",
    "\n",
    "    model = pyo.ConcreteModel()\n",
    "    model.x = pyo.Var(range(num_sets), domain=pyo.Binary)\n",
    "\n",
    "    @model.Constraint(entire_set)\n",
    "    def independent_rule(model, num):\n",
    "        return sum(model.x[idx] for idx in range(num_sets) if num in sub_sets[idx]) >= 1\n",
    "\n",
    "    model.cost = pyo.Objective(expr=sum(model.x.values()), sense=pyo.minimize)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffd32c78-9492-4ced-a5a0-9ba61d1b43b7",
   "metadata": {},
   "source": [
    "The model contains:\n",
    "\n",
    "- Binary variable for each subset (model.x) indicating if it is included in the sub-collection.\n",
    "- Objective rule \u2013 the size of the sub-collection.\n",
    "- Constraint \u2013 the sub-collection covers the original set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "69359e3e-ed85-4b56-9afb-9dd4b9997ada",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:36:45.038822Z",
     "iopub.status.busy": "2024-05-07T15:36:45.037683Z",
     "iopub.status.idle": "2024-05-07T15:36:45.054023Z",
     "shell.execute_reply": "2024-05-07T15:36:45.053234Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sub_sets = sub_sets = [\n",
    "    [1, 2, 3, 4],\n",
    "    [2, 3, 4, 5],\n",
    "    [6, 7],\n",
    "    [8, 9, 10],\n",
    "    [1, 6, 8],\n",
    "    [3, 7, 9],\n",
    "    [4, 7, 10],\n",
    "    [2, 5, 8],\n",
    "]\n",
    "\n",
    "set_cover_model = set_cover(sub_sets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a985b32-0670-42f3-ba50-5c0097eb75f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:36:45.059024Z",
     "iopub.status.busy": "2024-05-07T15:36:45.057842Z",
     "iopub.status.idle": "2024-05-07T15:36:45.067477Z",
     "shell.execute_reply": "2024-05-07T15:36:45.066797Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 Set Declarations\n",
      "    independent_rule_index : Size=1, Index=None, Ordered=False\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :   10 : {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}\n",
      "    x_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    8 : {0, 1, 2, 3, 4, 5, 6, 7}\n",
      "\n",
      "1 Var Declarations\n",
      "    x : Size=8, Index=x_index\n",
      "        Key : Lower : Value : Upper : Fixed : Stale : Domain\n",
      "          0 :     0 :  None :     1 : False :  True : Binary\n",
      "          1 :     0 :  None :     1 : False :  True : Binary\n",
      "          2 :     0 :  None :     1 : False :  True : Binary\n",
      "          3 :     0 :  None :     1 : False :  True : Binary\n",
      "          4 :     0 :  None :     1 : False :  True : Binary\n",
      "          5 :     0 :  None :     1 : False :  True : Binary\n",
      "          6 :     0 :  None :     1 : False :  True : Binary\n",
      "          7 :     0 :  None :     1 : False :  True : Binary\n",
      "\n",
      "1 Objective Declarations\n",
      "    cost : Size=1, Index=None, Active=True\n",
      "        Key  : Active : Sense    : Expression\n",
      "        None :   True : minimize : x[0] + x[1] + x[2] + x[3] + x[4] + x[5] + x[6] + x[7]\n",
      "\n",
      "1 Constraint Declarations\n",
      "    independent_rule : Size=10, Index=independent_rule_index, Active=True\n",
      "        Key : Lower : Body               : Upper : Active\n",
      "          1 :   1.0 :        x[0] + x[4] :  +Inf :   True\n",
      "          2 :   1.0 : x[0] + x[1] + x[7] :  +Inf :   True\n",
      "          3 :   1.0 : x[0] + x[1] + x[5] :  +Inf :   True\n",
      "          4 :   1.0 : x[0] + x[1] + x[6] :  +Inf :   True\n",
      "          5 :   1.0 :        x[1] + x[7] :  +Inf :   True\n",
      "          6 :   1.0 :        x[2] + x[4] :  +Inf :   True\n",
      "          7 :   1.0 : x[2] + x[5] + x[6] :  +Inf :   True\n",
      "          8 :   1.0 : x[3] + x[4] + x[7] :  +Inf :   True\n",
      "          9 :   1.0 :        x[3] + x[5] :  +Inf :   True\n",
      "         10 :   1.0 :        x[3] + x[6] :  +Inf :   True\n",
      "\n",
      "5 Declarations: x_index x independent_rule_index independent_rule cost\n"
     ]
    }
   ],
   "source": [
    "set_cover_model.pprint()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17ea14ec-dbb7-487c-b4f1-cabc8d5e3c29",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Setting Up the Classiq Problem Instance\n",
    "\n",
    "In order to solve the Pyomo model defined above, we use the Classiq combinatorial optimization engine. For the quantum part of the QAOA algorithm (`QAOAConfig`) - define the number of repetitions (`num_layers`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "816b468f-a59f-4f2f-8337-4a9d66548425",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:36:45.072198Z",
     "iopub.status.busy": "2024-05-07T15:36:45.071028Z",
     "iopub.status.idle": "2024-05-07T15:36:47.002020Z",
     "shell.execute_reply": "2024-05-07T15:36:47.001218Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import construct_combinatorial_optimization_model\n",
    "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n",
    "\n",
    "qaoa_config = QAOAConfig(num_layers=3, penalty_energy=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db34d5ac-6877-4285-8dec-7bf7b37eb783",
   "metadata": {},
   "source": [
    "For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`max_iteration`) and the $\\alpha$-parameter (`alpha_cvar`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e41d0dd3-4135-4330-9ba3-c1b30c339a74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:36:47.007469Z",
     "iopub.status.busy": "2024-05-07T15:36:47.006012Z",
     "iopub.status.idle": "2024-05-07T15:36:47.011287Z",
     "shell.execute_reply": "2024-05-07T15:36:47.010661Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "optimizer_config = OptimizerConfig(max_iteration=60, alpha_cvar=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "214d6051-43b8-4b9d-8454-f9cdb62b4cf0",
   "metadata": {},
   "source": [
    "Lastly, we load the model, based on the problem and algorithm parameters, which we can use to solve the problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0243019c-6fc3-435f-b6ec-8b4355d6660c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:36:47.015793Z",
     "iopub.status.busy": "2024-05-07T15:36:47.014626Z",
     "iopub.status.idle": "2024-05-07T15:36:49.435098Z",
     "shell.execute_reply": "2024-05-07T15:36:49.434274Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "qmod = construct_combinatorial_optimization_model(\n",
    "    pyo_model=set_cover_model,\n",
    "    qaoa_config=qaoa_config,\n",
    "    optimizer_config=optimizer_config,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fcc3812-c9d0-421c-84bb-38047297b33f",
   "metadata": {},
   "source": [
    "We also set the quantum backend we want to execute on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "53bc041f-065c-44d2-b220-dafd9d0504ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:36:49.440751Z",
     "iopub.status.busy": "2024-05-07T15:36:49.439278Z",
     "iopub.status.idle": "2024-05-07T15:36:49.569569Z",
     "shell.execute_reply": "2024-05-07T15:36:49.568764Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import set_execution_preferences\n",
    "from classiq.execution import ClassiqBackendPreferences, ExecutionPreferences\n",
    "\n",
    "backend_preferences = ExecutionPreferences(\n",
    "    backend_preferences=ClassiqBackendPreferences(backend_name=\"aer_simulator\")\n",
    ")\n",
    "\n",
    "qmod = set_execution_preferences(qmod, backend_preferences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4f2734e8-3b97-4298-8afe-640044685896",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:36:49.574677Z",
     "iopub.status.busy": "2024-05-07T15:36:49.573452Z",
     "iopub.status.idle": "2024-05-07T15:36:49.710082Z",
     "shell.execute_reply": "2024-05-07T15:36:49.709369Z"
    }
   },
   "outputs": [],
   "source": [
    "from classiq import write_qmod\n",
    "\n",
    "write_qmod(qmod, \"set_cover\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "943291f0-6a9f-4286-a69d-ef13a0a12ef6",
   "metadata": {},
   "source": [
    "## Synthesizing the QAOA Circuit and Solving the Problem\n",
    "\n",
    "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1d71e29a-5d53-49c4-84b2-45f59be4da31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:36:49.714846Z",
     "iopub.status.busy": "2024-05-07T15:36:49.713781Z",
     "iopub.status.idle": "2024-05-07T15:36:57.571614Z",
     "shell.execute_reply": "2024-05-07T15:36:57.570935Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening: https://platform.classiq.io/circuit/11fd03ea-49d7-4ea9-a772-fca49f53953e?version=0.41.0.dev39%2B79c8fd0855\n"
     ]
    }
   ],
   "source": [
    "from classiq import show, synthesize\n",
    "\n",
    "qprog = synthesize(qmod)\n",
    "show(qprog)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80238cf9-d7bd-46e5-9d48-b7cf23a6b304",
   "metadata": {},
   "source": [
    "We now solve the problem by calling the `execute` function on the quantum program we have generated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "62d12d20-1c80-4a9e-bb6b-b1fddc6cbe40",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:36:57.574164Z",
     "iopub.status.busy": "2024-05-07T15:36:57.573795Z",
     "iopub.status.idle": "2024-05-07T15:47:48.605861Z",
     "shell.execute_reply": "2024-05-07T15:47:48.605172Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import execute\n",
    "\n",
    "res = execute(qprog).result()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "620ea6a0-cd05-41a9-a2ed-9631c680d2e6",
   "metadata": {},
   "source": [
    "We can check the convergence of the run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "02454398-b229-403c-824a-b1eb539fbc1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:47:48.614337Z",
     "iopub.status.busy": "2024-05-07T15:47:48.613888Z",
     "iopub.status.idle": "2024-05-07T15:47:48.663145Z",
     "shell.execute_reply": "2024-05-07T15:47:48.662502Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": "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",
      "image/png": "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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from classiq.execution import VQESolverResult\n",
    "\n",
    "vqe_result = res[0].value\n",
    "vqe_result.convergence_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "615ed612-b835-4bf0-aa92-92d30ef8006d",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Optimization Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "670eddd3-2da7-4a88-b571-7884ef24f60c",
   "metadata": {},
   "source": [
    "We can also examine the statistics of the algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "516d78ba-2951-46eb-b1af-efe877513556",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:47:48.667430Z",
     "iopub.status.busy": "2024-05-07T15:47:48.666357Z",
     "iopub.status.idle": "2024-05-07T15:47:50.023109Z",
     "shell.execute_reply": "2024-05-07T15:47:50.022422Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probability</th>\n",
       "      <th>cost</th>\n",
       "      <th>solution</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>0.001</td>\n",
       "      <td>4.0</td>\n",
       "      <td>[0, 0, 0, 0, 1, 1, 1, 1]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>0.001</td>\n",
       "      <td>14.0</td>\n",
       "      <td>[0, 0, 0, 0, 1, 1, 1, 1]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>0.001</td>\n",
       "      <td>14.0</td>\n",
       "      <td>[0, 0, 0, 0, 1, 1, 1, 1]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>0.001</td>\n",
       "      <td>15.0</td>\n",
       "      <td>[0, 0, 0, 1, 1, 1, 1, 1]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.001</td>\n",
       "      <td>15.0</td>\n",
       "      <td>[1, 1, 1, 1, 1, 0, 0, 0]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     probability  cost                  solution  count\n",
       "118        0.001   4.0  [0, 0, 0, 0, 1, 1, 1, 1]      1\n",
       "986        0.001  14.0  [0, 0, 0, 0, 1, 1, 1, 1]      1\n",
       "524        0.001  14.0  [0, 0, 0, 0, 1, 1, 1, 1]      1\n",
       "142        0.001  15.0  [0, 0, 0, 1, 1, 1, 1, 1]      1\n",
       "37         0.001  15.0  [1, 1, 1, 1, 1, 0, 0, 0]      1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from classiq.applications.combinatorial_optimization import (\n",
    "    get_optimization_solution_from_pyo,\n",
    ")\n",
    "\n",
    "solution = get_optimization_solution_from_pyo(\n",
    "    set_cover_model, vqe_result=vqe_result, penalty_energy=qaoa_config.penalty_energy\n",
    ")\n",
    "optimization_result = pd.DataFrame.from_records(solution)\n",
    "optimization_result.sort_values(by=\"cost\", ascending=True).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "687f492b-a4a5-49c6-964c-8959b035bb93",
   "metadata": {},
   "source": [
    "And the histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "31a4e74d-b2b8-42e0-826d-de7b51de1fe8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:47:50.025530Z",
     "iopub.status.busy": "2024-05-07T15:47:50.025337Z",
     "iopub.status.idle": "2024-05-07T15:47:50.337003Z",
     "shell.execute_reply": "2024-05-07T15:47:50.336242Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'cost'}>]], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiwAAAGzCAYAAAAMr0ziAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAuqklEQVR4nO3de3DU9b3/8dcmJBsiRsBANkEk4VIRuUSJyYnF4tSQhFIL9TIBnAEzDkzVjHq2RY0/SMLlTChSTrRyzGiHiq0IRVs8rTQQo7HHEoNcPBxAOIDQlMuGiycsJGWzZr+/PxzWrgmQDQv7YfN8zOwk+/l+vp/9fN75rrz87nd3bZZlWQIAADBYVLgnAAAAcCkEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILgIizadMmlZWVqampKdxTARAiBBYAEWfTpk2aP38+gQWIIAQWAABgPAILgKvmyJEjevTRR5WSkiK73a60tDQ99thjam1tlSR98cUXeuihh9S3b1/Fx8frX/7lX/Tee++1G+eXv/ylbrvtNsXHx6tPnz7KyMjQqlWrJEllZWWaM2eOJCktLU02m002m02HDh26ausEEHo9wj0BAN3D0aNHlZmZqaamJs2ePVvDhw/XkSNH9Pbbb6ulpUX/93//p7vuukstLS168skndeONN2rlypX60Y9+pLfffls//vGPJUmvvfaannzyST344IN66qmndO7cOe3YsUP19fWaPn267r//fv3v//6v3nrrLf37v/+7EhMTJUn9+vUL5/IBXCabZVlWuCcBIPLNnDlTv/3tb1VfX6+MjIyAbZZlyel0qqKiQv/1X/+lcePGSZLOnj2r0aNHy7IsHThwQFFRUZoyZYr279+vnTt3XvCxli5dqjlz5ujgwYNKTU29kssCcJXwkhCAK87n82ndunW677772oUVSbLZbFq/fr0yMzP9YUWSevXqpdmzZ+vQoUPavXu3JKl37946fPiwPv3006s2fwDhR2ABcMWdOHFCbrdbI0eOvGCfv/3tb7rlllvatd96663+7ZL07LPPqlevXsrMzNSwYcP0xBNP6K9//euVmTgAYxBYAFxTbr31Vu3du1erV6/WuHHj9M4772jcuHEqLS0N99QAXEEEFgBXXL9+/ZSQkHDR604GDRqkvXv3tmvfs2ePf/t51113nQoKCvTrX/9aDQ0NmjRpkv7t3/5N586dk/T1S0wAIguBBcAVd/5i2T/+8Y/asmVLu+2WZekHP/iBNm/erLq6On97c3OzXn31VaWmpmrEiBGSpFOnTgXsGxsbqxEjRsiyLHm9XklfBxpJfHAcEEF4lxCAq+LIkSPKyMiQ2+3W7Nmzdeutt+rYsWNau3atPv74Y3k8Ho0ZM0bnzp3Tk08+qb59+2rlypX67//+b73zzjv+tzWPHTtWDodD3/3ud5WUlKTPP/9cL7/8snJzc/Wf//mfkqRPP/1UmZmZ+sEPfqCpU6cqJiZG9913nz/IALj2EFgAXDUNDQ2aN2+e/vznP8vtdmvAgAGaOHGili1bptjYWH3xxRd69tln9f777+vcuXMaPXq0SkpKNGnSJP8Yr776qt58803t2rVLZ8+e1U033aT7779fc+fOVUJCgr/fokWLVFlZqWPHjsnn8/EWZ+AaR2ABAADG4xoWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADj9Qj3BELB5/Pp6NGjuv766/lIbgAArhGWZenMmTNKSUlRVNTFz6FERGA5evSoBg4cGO5pAACALvj73/+um2666aJ9IiKwXH/99ZK+XvA/f9JlZ3m9Xm3cuFG5ubmKiYkJ9fS6JWoaetQ09KhpaFHP0Iv0mrrdbg0cOND/7/jFRERgOf8yUEJCQpcDS3x8vBISEiLygAgHahp61DT0qGloUc/Q6y417czlHFx0CwAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxuhRYli9frtTUVMXFxSkrK0ubN2++YN/f//73ysjIUO/evXXdddcpPT1dv/nNbwL6WJalkpISJScnq2fPnsrJydG+ffu6MjUAABCBgg4sa9askdPpVGlpqbZt26YxY8YoLy9Px48f77B/37599f/+3/9TXV2dduzYocLCQhUWFmrDhg3+PkuWLNFLL72kyspK1dfX67rrrlNeXp7OnTvX9ZUBAICI0SPYHZYtW6ZZs2apsLBQklRZWan33ntPK1as0HPPPdeu/z333BNw/6mnntLKlSv18ccfKy8vT5ZlqaKiQnPnztXkyZMlSW+88YaSkpK0bt06TZ06td2YHo9HHo/Hf9/tdkv6+mu4vV5vsEvy79OVfU01smzDpTtdQfYoSwszpLELquTxXfprwyVpZ1neFZ7VtS0Sj9Nwo6ahRT1DL9JrGsy6bJZlWZ3t3Nraqvj4eL399tuaMmWKv33mzJlqamrSu+++e9H9LcvSBx98oB/96Edat26dJkyYoC+++EJDhgzR9u3blZ6e7u87fvx4paen68UXX2w3TllZmebPn9+ufdWqVYqPj+/scgAAQBi1tLRo+vTpOn36tBISEi7aN6gzLCdPnlRbW5uSkpIC2pOSkrRnz54L7nf69GkNGDBAHo9H0dHR+o//+A9NmDBBkuRyufxjfHvM89u+rbi4WE6n03/f7XZr4MCBys3NveSCO+L1elVdXa0JEyYoJiYm6P1NZMYZFp/mbYniDEuIROJxGm7UNLSoZ+hFek3Pv0LSGUG/JNQV119/vT777DOdPXtWNTU1cjqdGjx4cLuXizrLbrfLbre3a4+JibmsP+jl7m8ST1vnQsKV5vHZOj2XSKn9lRZJx6kpqGloUc/Qi9SaBrOmoAJLYmKioqOj1djYGNDe2Ngoh8Nxwf2ioqI0dOhQSVJ6ero+//xzlZeX65577vHv19jYqOTk5IAx//klIgAA0H0F9S6h2NhYjR07VjU1Nf42n8+nmpoaZWdnd3ocn8/nv2g2LS1NDocjYEy32636+vqgxgQAAJEr6JeEnE6nZs6cqYyMDGVmZqqiokLNzc3+dw3NmDFDAwYMUHl5uSSpvLxcGRkZGjJkiDwej9avX6/f/OY3euWVVyRJNptNTz/9tBYtWqRhw4YpLS1N8+bNU0pKSsCFvQAAoPsKOrAUFBToxIkTKikpkcvlUnp6uqqqqvwXzTY0NCgq6psTN83NzXr88cd1+PBh9ezZU8OHD9dvf/tbFRQU+Ps888wzam5u1uzZs9XU1KRx48apqqpKcXFxIVgiAAC41nXpotuioiIVFRV1uK22tjbg/qJFi7Ro0aKLjmez2bRgwQItWLCgK9MBAAARju8SAgAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPF6hHsCwHmpz70X7ikE7dDiSeGeAgB0C5xhAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8boUWJYvX67U1FTFxcUpKytLmzdvvmDf1157TXfffbf69OmjPn36KCcnp13/Rx55RDabLeCWn5/flakBAIAIFHRgWbNmjZxOp0pLS7Vt2zaNGTNGeXl5On78eIf9a2trNW3aNH344Yeqq6vTwIEDlZubqyNHjgT0y8/P17Fjx/y3t956q2srAgAAESfowLJs2TLNmjVLhYWFGjFihCorKxUfH68VK1Z02P/NN9/U448/rvT0dA0fPly/+tWv5PP5VFNTE9DPbrfL4XD4b3369OnaigAAQMTpEUzn1tZWbd26VcXFxf62qKgo5eTkqK6urlNjtLS0yOv1qm/fvgHttbW16t+/v/r06aPvf//7WrRokW688cYOx/B4PPJ4PP77brdbkuT1euX1eoNZkn+/f/4ZCezRVngfP8oK+BmpruYxE4nHabhR09CinqEX6TUNZl02y7I6/S/K0aNHNWDAAG3atEnZ2dn+9meeeUYfffSR6uvrLznG448/rg0bNmjXrl2Ki4uTJK1evVrx8fFKS0vTgQMH9Pzzz6tXr16qq6tTdHR0uzHKyso0f/78du2rVq1SfHx8Z5cDAADCqKWlRdOnT9fp06eVkJBw0b5BnWG5XIsXL9bq1atVW1vrDyuSNHXqVP/vo0aN0ujRozVkyBDV1tbq3nvvbTdOcXGxnE6n/77b7fZfG3OpBXfE6/WqurpaEyZMUExMTND7m2hk2YawPr49ytLCDJ/mbYmSx2cL61yupJ1leVftsSLxOA03ahpa1DP0Ir2m518h6YygAktiYqKio6PV2NgY0N7Y2CiHw3HRfZcuXarFixfr/fff1+jRoy/ad/DgwUpMTNT+/fs7DCx2u112u71de0xMzGX9QS93f5N42swICR6fzZi5XAnhOF4i6Tg1BTUNLeoZepFa02DWFNRFt7GxsRo7dmzABbPnL6D955eIvm3JkiVauHChqqqqlJGRccnHOXz4sE6dOqXk5ORgpgcAACJU0O8Scjqdeu2117Ry5Up9/vnneuyxx9Tc3KzCwkJJ0owZMwIuyv35z3+uefPmacWKFUpNTZXL5ZLL5dLZs2clSWfPntWcOXP0ySef6NChQ6qpqdHkyZM1dOhQ5eVdvdPtAADAXEFfw1JQUKATJ06opKRELpdL6enpqqqqUlJSkiSpoaFBUVHf5KBXXnlFra2tevDBBwPGKS0tVVlZmaKjo7Vjxw6tXLlSTU1NSklJUW5urhYuXNjhyz4AAKD76dJFt0VFRSoqKupwW21tbcD9Q4cOXXSsnj17asOG8F4gCgAAzMZ3CQEAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMbrUmBZvny5UlNTFRcXp6ysLG3evPmCfV977TXdfffd6tOnj/r06aOcnJx2/S3LUklJiZKTk9WzZ0/l5ORo3759XZkaAACIQEEHljVr1sjpdKq0tFTbtm3TmDFjlJeXp+PHj3fYv7a2VtOmTdOHH36ouro6DRw4ULm5uTpy5Ii/z5IlS/TSSy+psrJS9fX1uu6665SXl6dz5851fWUAACBi9Ah2h2XLlmnWrFkqLCyUJFVWVuq9997TihUr9Nxzz7Xr/+abbwbc/9WvfqV33nlHNTU1mjFjhizLUkVFhebOnavJkydLkt544w0lJSVp3bp1mjp1arsxPR6PPB6P/77b7ZYkeb1eeb3eYJfk36cr+5rKHm2F9/GjrICfkepqHjOReJyGGzUNLeoZepFe02DWZbMsq9P/orS2tio+Pl5vv/22pkyZ4m+fOXOmmpqa9O67715yjDNnzqh///5au3atfvjDH+qLL77QkCFDtH37dqWnp/v7jR8/Xunp6XrxxRfbjVFWVqb58+e3a1+1apXi4+M7uxwAABBGLS0tmj59uk6fPq2EhISL9g3qDMvJkyfV1tampKSkgPakpCTt2bOnU2M8++yzSklJUU5OjiTJ5XL5x/j2mOe3fVtxcbGcTqf/vtvt9r/UdKkFd8Tr9aq6uloTJkxQTExM0PubaGTZhrA+vj3K0sIMn+ZtiZLHZwvrXK6knWV5V+2xIvE4DTdqGlrUM/QivabnXyHpjKBfErocixcv1urVq1VbW6u4uLguj2O322W329u1x8TEXNYf9HL3N4mnzYyQ4PHZjJnLlRCO4yWSjlNTUNPQop6hF6k1DWZNQV10m5iYqOjoaDU2Nga0NzY2yuFwXHTfpUuXavHixdq4caNGjx7tbz+/X1fGBAAA3UNQgSU2NlZjx45VTU2Nv83n86mmpkbZ2dkX3G/JkiVauHChqqqqlJGREbAtLS1NDocjYEy32636+vqLjgkAALqPoF8ScjqdmjlzpjIyMpSZmamKigo1Nzf73zU0Y8YMDRgwQOXl5ZKkn//85yopKdGqVauUmprqvy6lV69e6tWrl2w2m55++mktWrRIw4YNU1pamubNm6eUlJSAC3sBAED3FXRgKSgo0IkTJ1RSUiKXy6X09HRVVVX5L5ptaGhQVNQ3J25eeeUVtba26sEHHwwYp7S0VGVlZZKkZ555Rs3NzZo9e7aampo0btw4VVVVXdZ1LgAAIHJ06aLboqIiFRUVdbittrY24P6hQ4cuOZ7NZtOCBQu0YMGCrkwHAABEOL5LCAAAGI/AAgAAjEdgAQAAxiOwAAAA413VT7oFIk3qc+9dtceyR1takvn11y5czqcHH1o8KYSzAoCrgzMsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYLwuBZbly5crNTVVcXFxysrK0ubNmy/Yd9euXXrggQeUmpoqm82mioqKdn3Kyspks9kCbsOHD+/K1AAAQAQKOrCsWbNGTqdTpaWl2rZtm8aMGaO8vDwdP368w/4tLS0aPHiwFi9eLIfDccFxb7vtNh07dsx/+/jjj4OdGgAAiFBBB5Zly5Zp1qxZKiws1IgRI1RZWan4+HitWLGiw/533nmnXnjhBU2dOlV2u/2C4/bo0UMOh8N/S0xMDHZqAAAgQvUIpnNra6u2bt2q4uJif1tUVJRycnJUV1d3WRPZt2+fUlJSFBcXp+zsbJWXl+vmm2/usK/H45HH4/Hfd7vdkiSv1yuv1xv0Y5/fpyv7msoebYX38aOsgJ+4fKGqaSQd55crEp/74UQ9Qy/SaxrMuoIKLCdPnlRbW5uSkpIC2pOSkrRnz55ghgqQlZWl119/XbfccouOHTum+fPn6+6779bOnTt1/fXXt+tfXl6u+fPnt2vfuHGj4uPjuzyP6urqLu9rmiWZ4Z7B1xZm+MI9hYhzuTVdv359iGYSOSLpuW8C6hl6kVrTlpaWTvcNKrBcKRMnTvT/Pnr0aGVlZWnQoEH63e9+p0cffbRd/+LiYjmdTv99t9utgQMHKjc3VwkJCUE/vtfrVXV1tSZMmKCYmJiuLcIwI8s2hPXx7VGWFmb4NG9LlDw+W1jnEilCVdOdZXkhnNW1LRKf++FEPUMv0mt6/hWSzggqsCQmJio6OlqNjY0B7Y2NjRe9oDZYvXv31ne+8x3t37+/w+12u73D62FiYmIu6w96ufubxNNmRkjw+GzGzCVSXG5NI+UYD6VIeu6bgHqGXqTWNJg1BXXRbWxsrMaOHauamhp/m8/nU01NjbKzs4MZ6qLOnj2rAwcOKDk5OWRjAgCAa1fQLwk5nU7NnDlTGRkZyszMVEVFhZqbm1VYWChJmjFjhgYMGKDy8nJJX1+ou3v3bv/vR44c0WeffaZevXpp6NChkqSf/exnuu+++zRo0CAdPXpUpaWlio6O1rRp00K1TgAAcA0LOrAUFBToxIkTKikpkcvlUnp6uqqqqvwX4jY0NCgq6psTN0ePHtXtt9/uv7906VItXbpU48ePV21trSTp8OHDmjZtmk6dOqV+/fpp3Lhx+uSTT9SvX7/LXB4AAIgEXbrotqioSEVFRR1uOx9CzktNTZVlXfxtmKtXr+7KNAAAQDfBdwkBAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYLwe4Z4AgKsr9bn3wj2FoB1aPCncUwAQZpxhAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjdSmwLF++XKmpqYqLi1NWVpY2b958wb67du3SAw88oNTUVNlsNlVUVFz2mAAAoHsJOrCsWbNGTqdTpaWl2rZtm8aMGaO8vDwdP368w/4tLS0aPHiwFi9eLIfDEZIxAQBA9xJ0YFm2bJlmzZqlwsJCjRgxQpWVlYqPj9eKFSs67H/nnXfqhRde0NSpU2W320MyJgAA6F6C+rbm1tZWbd26VcXFxf62qKgo5eTkqK6urksT6MqYHo9HHo/Hf9/tdkuSvF6vvF5v0HM4v09X9jWVPdoK7+NHWQE/cfm6c02v1HMzEp/74UQ9Qy/SaxrMuoIKLCdPnlRbW5uSkpIC2pOSkrRnz55ghrqsMcvLyzV//vx27Rs3blR8fHyX5iFJ1dXVXd7XNEsywz2Dry3M8IV7ChGnO9Z0/fr1V3T8SHrum4B6hl6k1rSlpaXTfYMKLKYoLi6W0+n033e73Ro4cKByc3OVkJAQ9Hher1fV1dWaMGGCYmJiQjnVsBlZtiGsj2+PsrQww6d5W6Lk8dnCOpdI0Z1rurMs74qMG4nP/XCinqEX6TU9/wpJZwQVWBITExUdHa3GxsaA9sbGxgteUHslxrTb7R1eDxMTE3NZf9DL3d8knjYz/kHz+GzGzCVSdMeaXunnZSQ9901APUMvUmsazJqCuug2NjZWY8eOVU1Njb/N5/OppqZG2dnZwQx1RccEAACRJeiXhJxOp2bOnKmMjAxlZmaqoqJCzc3NKiwslCTNmDFDAwYMUHl5uaSvL6rdvXu3//cjR47os88+U69evTR06NBOjQkAALq3oANLQUGBTpw4oZKSErlcLqWnp6uqqsp/0WxDQ4Oior45cXP06FHdfvvt/vtLly7V0qVLNX78eNXW1nZqTAAA0L116aLboqIiFRUVdbjtfAg5LzU1VZZ16bdhXmxMAADQvfFdQgAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPG6FFiWL1+u1NRUxcXFKSsrS5s3b75o/7Vr12r48OGKi4vTqFGjtH79+oDtjzzyiGw2W8AtPz+/K1MDAAARKOjAsmbNGjmdTpWWlmrbtm0aM2aM8vLydPz48Q77b9q0SdOmTdOjjz6q7du3a8qUKZoyZYp27twZ0C8/P1/Hjh3z3956662urQgAAEScoAPLsmXLNGvWLBUWFmrEiBGqrKxUfHy8VqxY0WH/F198Ufn5+ZozZ45uvfVWLVy4UHfccYdefvnlgH52u10Oh8N/69OnT9dWBAAAIk6PYDq3trZq69atKi4u9rdFRUUpJydHdXV1He5TV1cnp9MZ0JaXl6d169YFtNXW1qp///7q06ePvv/972vRokW68cYbOxzT4/HI4/H477vdbkmS1+uV1+sNZkn+/f75ZySwR1vhffwoK+AnLl93rumVem5G4nM/nKhn6EV6TYNZV1CB5eTJk2pra1NSUlJAe1JSkvbs2dPhPi6Xq8P+LpfLfz8/P1/333+/0tLSdODAAT3//POaOHGi6urqFB0d3W7M8vJyzZ8/v137xo0bFR8fH8ySAlRXV3d5X9MsyQz3DL62MMMX7ilEnO5Y029f9xZqkfTcNwH1DL1IrWlLS0un+wYVWK6UqVOn+n8fNWqURo8erSFDhqi2tlb33ntvu/7FxcUBZ23cbrcGDhyo3NxcJSQkBP34Xq9X1dXVmjBhgmJiYrq2CMOMLNsQ1se3R1lamOHTvC1R8vhsYZ1LpOjONd1ZlndFxo3E5344Uc/Qi/Sann+FpDOCCiyJiYmKjo5WY2NjQHtjY6McDkeH+zgcjqD6S9LgwYOVmJio/fv3dxhY7Ha77HZ7u/aYmJjL+oNe7v4m8bSZ8Q+ax2czZi6RojvWdNi8jVdkXHu0pSWZ0u3/9sEVqemhxZNCPua1IJL+W2qKSK1pMGsK6qLb2NhYjR07VjU1Nf42n8+nmpoaZWdnd7hPdnZ2QH/p61NbF+ovSYcPH9apU6eUnJwczPQAAECECvpdQk6nU6+99ppWrlypzz//XI899piam5tVWFgoSZoxY0bARblPPfWUqqqq9Itf/EJ79uxRWVmZtmzZoqKiIknS2bNnNWfOHH3yySc6dOiQampqNHnyZA0dOlR5eVfmNDAAALi2BH0NS0FBgU6cOKGSkhK5XC6lp6erqqrKf2FtQ0ODoqK+yUF33XWXVq1apblz5+r555/XsGHDtG7dOo0cOVKSFB0drR07dmjlypVqampSSkqKcnNztXDhwg5f9gEAAN1Ply66LSoq8p8h+bba2tp2bQ899JAeeuihDvv37NlTGzaE9wJRAABgNr5LCAAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYr0ufdNvdpD73XrinAABAt8YZFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMbrEe4JAECkSn3uvXBPIWiHFk8K9xSADnGGBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHh8cBwDwu5wPu7NHW1qSKY0s2yBPmy2Es7o4Puyue+AMCwAAMB6BBQAAGI/AAgAAjEdgAQAAxuOiWwDANY1vxe4eOMMCAACM16XAsnz5cqWmpiouLk5ZWVnavHnzRfuvXbtWw4cPV1xcnEaNGqX169cHbLcsSyUlJUpOTlbPnj2Vk5Ojffv2dWVqAAAgAgX9ktCaNWvkdDpVWVmprKwsVVRUKC8vT3v37lX//v3b9d+0aZOmTZum8vJy/fCHP9SqVas0ZcoUbdu2TSNHjpQkLVmyRC+99JJWrlyptLQ0zZs3T3l5edq9e7fi4uIuf5UAABiksy9jheuzbToS7pexgj7DsmzZMs2aNUuFhYUaMWKEKisrFR8frxUrVnTY/8UXX1R+fr7mzJmjW2+9VQsXLtQdd9yhl19+WdLXZ1cqKio0d+5cTZ48WaNHj9Ybb7yho0ePat26dZe1OAAAEBmCOsPS2tqqrVu3qri42N8WFRWlnJwc1dXVdbhPXV2dnE5nQFteXp4/jBw8eFAul0s5OTn+7TfccIOysrJUV1enqVOnthvT4/HI4/H4758+fVqS9OWXX8rr9QazJEmS1+tVS0uLTp06pZiYmHbbe3zVHPSY3V0Pn6WWFp96eKPU5gvv/xVECmoaetQ0tKhn6JlU01OnToV8zDNnzkj6+uTFpQQVWE6ePKm2tjYlJSUFtCclJWnPnj0d7uNyuTrs73K5/NvPt12oz7eVl5dr/vz57drT0tI6txBcFdPDPYEIRE1Dj5qGFvUMPVNqmviLKzf2mTNndMMNN1y0zzX5tubi4uKAszY+n09ffvmlbrzxRtlswSdQt9utgQMH6u9//7sSEhJCOdVui5qGHjUNPWoaWtQz9CK9ppZl6cyZM0pJSblk36ACS2JioqKjo9XY2BjQ3tjYKIfD0eE+Dofjov3P/2xsbFRycnJAn/T09A7HtNvtstvtAW29e/cOZikdSkhIiMgDIpyoaehR09CjpqFFPUMvkmt6qTMr5wV10W1sbKzGjh2rmpoaf5vP51NNTY2ys7M73Cc7OzugvyRVV1f7+6elpcnhcAT0cbvdqq+vv+CYAACgewn6JSGn06mZM2cqIyNDmZmZqqioUHNzswoLCyVJM2bM0IABA1ReXi5JeuqppzR+/Hj94he/0KRJk7R69Wpt2bJFr776qiTJZrPp6aef1qJFizRs2DD/25pTUlI0ZcqU0K0UAABcs4IOLAUFBTpx4oRKSkrkcrmUnp6uqqoq/0WzDQ0Nior65sTNXXfdpVWrVmnu3Ll6/vnnNWzYMK1bt87/GSyS9Mwzz6i5uVmzZ89WU1OTxo0bp6qqqqv2GSx2u12lpaXtXmZC11HT0KOmoUdNQ4t6hh41/YbN6sx7iQAAAMKI7xICAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8Aouk5cuXKzU1VXFxccrKytLmzZvDPaVrQllZmWw2W8Bt+PDh/u3nzp3TE088oRtvvFG9evXSAw880O5Tj7u7v/zlL7rvvvuUkpIim83W7hvKLctSSUmJkpOT1bNnT+Xk5Gjfvn0Bfb788ks9/PDDSkhIUO/evfXoo4/q7NmzV3EVZrlUTR955JF2x21+fn5AH2r6jfLyct155526/vrr1b9/f02ZMkV79+4N6NOZ53pDQ4MmTZqk+Ph49e/fX3PmzNFXX311NZdijM7U9J577ml3nP7kJz8J6NPdatrtA8uaNWvkdDpVWlqqbdu2acyYMcrLy9Px48fDPbVrwm233aZjx475bx9//LF/27/+67/qj3/8o9auXauPPvpIR48e1f333x/G2ZqnublZY8aM0fLlyzvcvmTJEr300kuqrKxUfX29rrvuOuXl5encuXP+Pg8//LB27dql6upq/elPf9Jf/vIXzZ49+2otwTiXqqkk5efnBxy3b731VsB2avqNjz76SE888YQ++eQTVVdXy+v1Kjc3V83N33yL/aWe621tbZo0aZJaW1u1adMmrVy5Uq+//rpKSkrCsaSw60xNJWnWrFkBx+mSJUv827plTa1uLjMz03riiSf899va2qyUlBSrvLw8jLO6NpSWllpjxozpcFtTU5MVExNjrV271t/2+eefW5Ksurq6qzTDa4sk6w9/+IP/vs/nsxwOh/XCCy/425qamiy73W699dZblmVZ1u7duy1J1qeffurv8+c//9my2WzWkSNHrtrcTfXtmlqWZc2cOdOaPHnyBfehphd3/PhxS5L10UcfWZbVuef6+vXrraioKMvlcvn7vPLKK1ZCQoLl8Xiu7gIM9O2aWpZljR8/3nrqqacuuE93rGm3PsPS2tqqrVu3Kicnx98WFRWlnJwc1dXVhXFm1459+/YpJSVFgwcP1sMPP6yGhgZJ0tatW+X1egNqO3z4cN18883UtpMOHjwol8sVUMMbbrhBWVlZ/hrW1dWpd+/eysjI8PfJyclRVFSU6uvrr/qcrxW1tbXq37+/brnlFj322GM6deqUfxs1vbjTp09Lkvr27Supc8/1uro6jRo1yv+J6JKUl5cnt9utXbt2XcXZm+nbNT3vzTffVGJiokaOHKni4mK1tLT4t3XHmgb90fyR5OTJk2prawv4g0tSUlKS9uzZE6ZZXTuysrL0+uuv65ZbbtGxY8c0f/583X333dq5c6dcLpdiY2PbfYt2UlKSXC5XeCZ8jTlfp46Oz/PbXC6X+vfvH7C9R48e6tu3L3W+gPz8fN1///1KS0vTgQMH9Pzzz2vixImqq6tTdHQ0Nb0In8+np59+Wt/97nf9X6/Smee6y+Xq8Dg+v60766imkjR9+nQNGjRIKSkp2rFjh5599lnt3btXv//97yV1z5p268CCyzNx4kT/76NHj1ZWVpYGDRqk3/3ud+rZs2cYZwZc2NSpU/2/jxo1SqNHj9aQIUNUW1ure++9N4wzM98TTzyhnTt3BlyrhstzoZr+8zVTo0aNUnJysu69914dOHBAQ4YMudrTNEK3fkkoMTFR0dHR7a5mb2xslMPhCNOsrl29e/fWd77zHe3fv18Oh0Otra1qamoK6ENtO+98nS52fDocjnYXiH/11Vf68ssvqXMnDR48WImJidq/f78kanohRUVF+tOf/qQPP/xQN910k7+9M891h8PR4XF8flt3daGadiQrK0uSAo7T7lbTbh1YYmNjNXbsWNXU1PjbfD6fampqlJ2dHcaZXZvOnj2rAwcOKDk5WWPHjlVMTExAbffu3auGhgZq20lpaWlyOBwBNXS73aqvr/fXMDs7W01NTdq6dau/zwcffCCfz+f/Dxwu7vDhwzp16pSSk5MlUdNvsyxLRUVF+sMf/qAPPvhAaWlpAds781zPzs7W//zP/wQEwerqaiUkJGjEiBFXZyEGuVRNO/LZZ59JUsBx2u1qGu6rfsNt9erVlt1ut15//XVr9+7d1uzZs63evXsHXHmNjv30pz+1amtrrYMHD1p//etfrZycHCsxMdE6fvy4ZVmW9ZOf/MS6+eabrQ8++MDasmWLlZ2dbWVnZ4d51mY5c+aMtX37dmv79u2WJGvZsmXW9u3brb/97W+WZVnW4sWLrd69e1vvvvuutWPHDmvy5MlWWlqa9Y9//MM/Rn5+vnX77bdb9fX11scff2wNGzbMmjZtWriWFHYXq+mZM2esn/3sZ1ZdXZ118OBB6/3337fuuOMOa9iwYda5c+f8Y1DTbzz22GPWDTfcYNXW1lrHjh3z31paWvx9LvVc/+qrr6yRI0daubm51meffWZVVVVZ/fr1s4qLi8OxpLC7VE33799vLViwwNqyZYt18OBB691337UGDx5sfe973/OP0R1r2u0Di2VZ1i9/+Uvr5ptvtmJjY63MzEzrk08+CfeUrgkFBQVWcnKyFRsbaw0YMMAqKCiw9u/f79/+j3/8w3r88cetPn36WPHx8daPf/xj69ixY2GcsXk+/PBDS1K728yZMy3L+vqtzfPmzbOSkpIsu91u3XvvvdbevXsDxjh16pQ1bdo0q1evXlZCQoJVWFhonTlzJgyrMcPFatrS0mLl5uZa/fr1s2JiYqxBgwZZs2bNavc/KNT0Gx3VUpL161//2t+nM8/1Q4cOWRMnTrR69uxpJSYmWj/96U8tr9d7lVdjhkvVtKGhwfre975n9e3b17Lb7dbQoUOtOXPmWKdPnw4Yp7vV1GZZlnX1zucAAAAEr1tfwwIAAK4NBBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMN7/BwR+ybHInOigAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a890a1-c5d4-409d-b9a3-d7ffd4fdd6c0",
   "metadata": {},
   "source": [
    "Let us plot the solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4326e84b-26f6-4ea9-a53b-090fb3658b8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:47:50.339966Z",
     "iopub.status.busy": "2024-05-07T15:47:50.339479Z",
     "iopub.status.idle": "2024-05-07T15:47:50.343195Z",
     "shell.execute_reply": "2024-05-07T15:47:50.342571Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "best_solution = optimization_result.solution[optimization_result.cost.idxmin()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0d941c26-bbc6-403d-90d7-c061458e099f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:47:50.345381Z",
     "iopub.status.busy": "2024-05-07T15:47:50.345195Z",
     "iopub.status.idle": "2024-05-07T15:47:50.348809Z",
     "shell.execute_reply": "2024-05-07T15:47:50.348140Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantum Solution: num_sets=4, sets=[[1, 6, 8], [3, 7, 9], [4, 7, 10], [2, 5, 8]]\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f\"Quantum Solution: num_sets={int(sum(best_solution))}, sets={[sub_sets[i] for i in range(len(best_solution)) if best_solution[i]]}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dde1905d-aeff-4297-a9d3-ad14910e6161",
   "metadata": {},
   "source": [
    "Lastly, we can compare to the classical solution of the problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5a7ca4b6-25a0-46dd-b5cc-de6a639a6f57",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:47:50.352085Z",
     "iopub.status.busy": "2024-05-07T15:47:50.351904Z",
     "iopub.status.idle": "2024-05-07T15:47:50.430503Z",
     "shell.execute_reply": "2024-05-07T15:47:50.429853Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model unknown\n",
      "\n",
      "  Variables:\n",
      "    x : Size=8, Index=x_index\n",
      "        Key : Lower : Value : Upper : Fixed : Stale : Domain\n",
      "          0 :     0 :   1.0 :     1 : False : False : Binary\n",
      "          1 :     0 :   1.0 :     1 : False : False : Binary\n",
      "          2 :     0 :   1.0 :     1 : False : False : Binary\n",
      "          3 :     0 :   1.0 :     1 : False : False : Binary\n",
      "          4 :     0 :   0.0 :     1 : False : False : Binary\n",
      "          5 :     0 :   0.0 :     1 : False : False : Binary\n",
      "          6 :     0 :   0.0 :     1 : False : False : Binary\n",
      "          7 :     0 :   0.0 :     1 : False : False : Binary\n",
      "\n",
      "  Objectives:\n",
      "    cost : Size=1, Index=None, Active=True\n",
      "        Key  : Active : Value\n",
      "        None :   True :   4.0\n",
      "\n",
      "  Constraints:\n",
      "    independent_rule : Size=10\n",
      "        Key : Lower : Body : Upper\n",
      "          1 :   1.0 :  1.0 :  None\n",
      "          2 :   1.0 :  2.0 :  None\n",
      "          3 :   1.0 :  2.0 :  None\n",
      "          4 :   1.0 :  2.0 :  None\n",
      "          5 :   1.0 :  1.0 :  None\n",
      "          6 :   1.0 :  1.0 :  None\n",
      "          7 :   1.0 :  1.0 :  None\n",
      "          8 :   1.0 :  1.0 :  None\n",
      "          9 :   1.0 :  1.0 :  None\n",
      "         10 :   1.0 :  1.0 :  None\n"
     ]
    }
   ],
   "source": [
    "from pyomo.opt import SolverFactory\n",
    "\n",
    "solver = SolverFactory(\"couenne\")\n",
    "solver.solve(set_cover_model)\n",
    "\n",
    "set_cover_model.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2e5c1b07-6060-455d-81ed-e48a2207c81b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:47:50.435103Z",
     "iopub.status.busy": "2024-05-07T15:47:50.433926Z",
     "iopub.status.idle": "2024-05-07T15:47:50.439457Z",
     "shell.execute_reply": "2024-05-07T15:47:50.438759Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "classical_solution = [\n",
    "    pyo.value(set_cover_model.x[i]) for i in range(len(set_cover_model.x))\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a7524894-b5c5-42d4-8f92-a019bef5e7da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:47:50.444336Z",
     "iopub.status.busy": "2024-05-07T15:47:50.443142Z",
     "iopub.status.idle": "2024-05-07T15:47:50.449865Z",
     "shell.execute_reply": "2024-05-07T15:47:50.449249Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classical Solution: num_sets=4, sets=[[1, 2, 3, 4], [2, 3, 4, 5], [6, 7], [8, 9, 10]]\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f\"Classical Solution: num_sets={int(sum(classical_solution))}, sets={[sub_sets[i] for i in range(len(classical_solution)) if classical_solution[i]]}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e82b5953-122a-4707-8ab6-f741f14f13a5",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "\n",
    "## References\n",
    "\n",
    "<a id='SetCoverWiki'>[1]</a>: [Set Cover Problem (Wikipedia)](https://en.wikipedia.org/wiki/Set_cover_problem)\n",
    "\n",
    "<a id='QAOA'>[2]</a>: [Farhi, Edward, Jeffrey Goldstone, and Sam Gutmann. \"A quantum approximate optimization algorithm.\" arXiv preprint arXiv:1411.4028 (2014).](https://arxiv.org/abs/1411.4028)\n",
    "\n",
    "<a id='cvar'>[3]</a>: [Barkoutsos, Panagiotis Kl, et al. \"Improving variational quantum optimization using CVaR.\" Quantum 4 (2020): 256.](https://arxiv.org/abs/1907.04769)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "a07aacdcc8a415e7643a2bc993226848ff70704ebef014f87460de9126b773d0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
